Abstract

A problem of emotion and cognition is considered within a unified theory of cognition. There is a strong case for modern cognitive models to take arousal component of emotion into account because of its significant influence on performance (e.g. the inverted-U effect). Several hypotheses have been proposed to explain this effect, but they have not been integrated into cognitive architectures. Based on the analysis of the ACT-R (Anderson & Lebiere, 1998) cognitive architecture the mechanisms that can be used to model this effect are identified. Then a model of the classical Yerkes and Dodson experiment is introduced. The model matches the data by modifying several parameters, particularly noise and goal value in the conflict resolution strategy. Thus, the model supports the idea that the character of decision making changes for different arousal and motivational states. The effect of these changes on learning is analysed using information theory. In particular, randomness in behaviour due to a noise increase leads to a faster entropy reduction. Thus, noise can improve learning in the initial stage of problem exploration or upon changes in the environment. Furthermore, dynamic motivation can optimise the expenditure of effort. Therefore, emotion may play an important role in adaptation of cognitive processes. It is argued that the current conflict resolution mechanism in ACT-R does not explain the dynamics suggested by the model. A new theory and algorithm are proposed that use posterior estimation of expected costs. There are three main contributions of the thesis: 1) Ways of including the effects of emotion and motivation into cognitive models; 2) The analysis of the role of emotion in learning and intelligence; and 3) The introduction of a new machine learning algorithm suitable for applications not only in cognitive modelling, but in other areas of computer science.